publications

2025

1. Physics-Learning AI Datamodel (PLAID) datasets: a collection of physics simulations for machine learning

   Fabien Casenave, Xavier Roynard, Brian Staber, and 8 more authors

   arXiv preprint arXiv:2505.02974, 2025
2. Scalable and adaptive prediction bands with kernel sum-of-squares

   Louis Allain, Sébastien Da Veiga, and Brian Staber

   arXiv preprint arXiv:2505.21039, 2025

2024

1. MMGP: a mesh morphing gaussian process-based machine learning method for regression of physical problems under nonparametrized geometrical variability

   Fabien Casenave, Brian Staber, and Xavier Roynard

   Advances in Neural Information Processing Systems, 2024
2. Learning signals defined on graphs with optimal transport and Gaussian process regression

   Raphaël Carpintero Perez, Sébastien Veiga, Josselin Garnier, and 1 more author

   arXiv e-prints, 2024
3. Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels

   Raphaël Carpintero Perez, Sébastien Da Veiga, Josselin Garnier, and 1 more author

   In International Conference on Artificial Intelligence and Statistics, 2024

2023

1. Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization

   Clément Bénard, Brian Staber, and Sébastien Da Veiga

   Advances in Neural Information Processing Systems, 2023

2022

1. Benchmarking Bayesian neural networks and evaluation metrics for regression tasks

   Brian Staber and Sébastien Da Veiga

   arXiv preprint arXiv:2206.06779, 2022
2. Quantitative performance evaluation of Bayesian neural networks

   Brian Staber and Sébastien Da Veiga

   2022

2021

1. Loss of ellipticity analysis in non-smooth plasticity

   B Staber, Samuel Forest, M Al Kotob, and 2 more authors

   International Journal of Solids and Structures, 2021

2019

1. Stochastic modeling and identification of a hyperelastic constitutive model for laminated composites

   Brian Staber, Johann Guilleminot, Christian Soize, and 2 more authors

   Computer Methods in Applied Mechanics and Engineering, 2019

2018

1. A random field model for anisotropic strain energy functions and its application for uncertainty quantification in vascular mechanics

   Brian Staber and Johann Guilleminot

   Computer Methods in Applied Mechanics and Engineering, 2018
2. Stochastic analysis, simulation and identification of hyperelastic constitutive equations

   Brian Staber

   Université Paris-Est, 2018

2017

1. Stochastic modeling of the Ogden class of stored energy functions for hyperelastic materials: the compressible case

   Brian Staber and Johann Guilleminot

   ZAMM-Journal of Applied Mathematics and Mechanics/Zeitschrift für Angewandte Mathematik und Mechanik, 2017
2. Stochastic hyperelastic constitutive laws and identification procedure for soft biological tissues with intrinsic variability

   B Staber and J Guilleminot

   Journal of the mechanical behavior of biomedical materials, 2017
3. Functional approximation and projection of stored energy functions in computational homogenization of hyperelastic materials: A probabilistic perspective

   B Staber and J Guilleminot

   Computer Methods in Applied Mechanics and Engineering, 2017
4. Stochastic modeling and generation of random fields of elasticity tensors: A unified information-theoretic approach

   Brian Staber and Johann Guilleminot

   Comptes Rendus. Mécanique, 2017

2015

1. Approximate solutions of Lagrange multipliers for information-theoretic random field models

   Brian Staber and Johann Guilleminot

   SIAM/ASA Journal on Uncertainty Quantification, 2015
2. Stochastic modeling of a class of stored energy functions for incompressible hyperelastic materials with uncertainties

   Brian Staber and Johann Guilleminot

   Comptes Rendus. Mécanique, 2015